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Updated documentation for v1.4 release
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# Sphinx build info version 1
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‎docs/build/html/_sources/diagnostic.rst.txt

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Diagnostic Information
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======================
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In :doc:`userguide`, we saw that the output of Py-BOBYQA returns a container which includes diagnostic information about the progress of the algorithm (:code:`soln.diagnostic_info`). This object is a `Pandas <http://pandas.pydata.org/>`_ DataFrame, with one row per iteration of the algorithm. In this section, we explain the meaning of each type of output (the columns of the DataFrame).
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In :doc:`userguide`, we saw that the output of Py-BOBYQA returns a container which includes diagnostic information about the progress of the algorithm (:code:`soln.diagnostic_info`). This object is a `Pandas <http://pandas.pydata.org/>`_ DataFrame, with one row per iteration of the algorithm. If Pandas is not available, it returns a dictionary where each key listed below has a list of values, one per iteration of the algorithm. In this section, we explain the meaning of each type of output (the columns of the DataFrame).
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To save this information to a CSV file, use:
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Version 1.4 (16 May 2023)
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-------------------------
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* Return diagnostic information as dictionary if Pandas not available (removes Pandas dependency)
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* Handle Nan/Inf values in model gradient and Hessian by gracefully exiting trust-region subproblem
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* Bugfix: automatically make model Hessian symmetric before trust-region subproblem with warning, instead of returning an error
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‎docs/build/html/_sources/index.rst.txt

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**Date:** |today|
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**Author:** `Lindon Roberts <lindon.roberts@maths.ox.ac.uk>`_
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**Author:** `Lindon Roberts <lindon.roberts@sydney.edu.au>`_
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Py-BOBYQA is a flexible package for finding local solutions to nonlinear, nonconvex minimization problems (with optional bound constraints), without requiring any derivatives of the objective. Py-BOBYQA is a Python implementation of the `BOBYQA <http://mat.uc.pt/~zhang/software.html#powell_software>`_ solver by Powell (documentation `here <http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf>`_). It is particularly useful when evaluations of the objective function are expensive and/or noisy.
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* `NumPy 1.11 or higher <http://www.numpy.org/>`_
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* `Pandas 0.17 or higher <https://pandas.pydata.org/>`_
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* Optionally, `Pandas 0.17 or higher <https://pandas.pydata.org/>`_, to return the diagnostic information as a DataFrame
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**Optional package:** Py-BOBYQA versions 1.2 and higher also support the `trustregion <https://github.com/lindonroberts/trust-region>`_ package for fast trust-region subproblem solutions. To install this, make sure you have a Fortran compiler (e.g. `gfortran <https://gcc.gnu.org/wiki/GFortran>`_) and NumPy installed, then run :code:`pip install trustregion`. You do not have to have trustregion installed for Py-BOBYQA to work, and it is not installed by default.
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<h1>Diagnostic Information<a class="headerlink" href="#diagnostic-information" title="Permalink to this headline"></a></h1>
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<p>In <a class="reference internal" href="userguide.html"><span class="doc">Using Py-BOBYQA</span></a>, we saw that the output of Py-BOBYQA returns a container which includes diagnostic information about the progress of the algorithm (<code class="code docutils literal notranslate"><span class="pre">soln.diagnostic_info</span></code>). This object is a <a class="reference external" href="http://pandas.pydata.org/">Pandas</a> DataFrame, with one row per iteration of the algorithm. In this section, we explain the meaning of each type of output (the columns of the DataFrame).</p>
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<p>In <a class="reference internal" href="userguide.html"><span class="doc">Using Py-BOBYQA</span></a>, we saw that the output of Py-BOBYQA returns a container which includes diagnostic information about the progress of the algorithm (<code class="code docutils literal notranslate"><span class="pre">soln.diagnostic_info</span></code>). This object is a <a class="reference external" href="http://pandas.pydata.org/">Pandas</a> DataFrame, with one row per iteration of the algorithm. If Pandas is not available, it returns a dictionary where each key listed below has a list of values, one per iteration of the algorithm. In this section, we explain the meaning of each type of output (the columns of the DataFrame).</p>
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<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Previously: define objfun and x0</span>

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<h2>Version 1.4 (16 May 2023)<a class="headerlink" href="#version-1-4-16-may-2023" title="Permalink to this headline"></a></h2>
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<h1>Py-BOBYQA: Derivative-Free Optimizer for Bound-Constrained Minimization<a class="headerlink" href="#py-bobyqa-derivative-free-optimizer-for-bound-constrained-minimization" title="Permalink to this headline"></a></h1>
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<p><strong>Release:</strong> 1.3</p>
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<p><strong>Date:</strong> 14 April 2021</p>
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<p><strong>Author:</strong> <a class="reference external" href="mailto:lindon&#46;roberts&#37;&#52;&#48;maths&#46;ox&#46;ac&#46;uk">Lindon Roberts</a></p>
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<p><strong>Release:</strong> 1.4</p>
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<p><strong>Date:</strong> 16 May 2023</p>
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<p><strong>Author:</strong> <a class="reference external" href="mailto:lindon&#46;roberts&#37;&#52;&#48;sydney&#46;edu&#46;au">Lindon Roberts</a></p>
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<p>Py-BOBYQA is a flexible package for finding local solutions to nonlinear, nonconvex minimization problems (with optional bound constraints), without requiring any derivatives of the objective. Py-BOBYQA is a Python implementation of the <a class="reference external" href="http://mat.uc.pt/~zhang/software.html#powell_software">BOBYQA</a> solver by Powell (documentation <a class="reference external" href="http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2009_06.pdf">here</a>). It is particularly useful when evaluations of the objective function are expensive and/or noisy.</p>
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